Abstract

The creation of surfaces from overlapping images taken from different vantages is a hard and important problem in computer vision. Recent developments fall primarily into two categories: the use of dense matching to produce point clouds from which surfaces are built, and the construction of surfaces from images directly. This paper presents a new method for surface reconstruction falling in the second category. First, a strongly motivated variational framework is built from the ground up based on a limiting case of photo-consistency. The framework includes a powerful new edge preserving smoothness term and exploits the input im- ages exhaustively, directly yielding high quality surfaces instead of deal- ing with issues (such as noise or misalignment) after the fact. Numeric solution is accomplished with a combination of Gauss-Newton descent and the finite element method, yielding deep convergence in few iterates. The method is fast, robust, very insensitive to view/scene configurations, and produces state-of-the-art results in the Middlebury evaluation.